_base_ = [
    '../_base_/datasets/cuhk_detection_1000.py',
    # '../_base_/schedules/schedule_1x.py',
    "/disk2t/fby/new_version_add_oldversion_modify/configs/_base_/schedules/alignps_schedule.py",
    '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
# model settings
model = dict(
    type='FCOSReid',
    backbone=dict(
        # _delete_=True,
        type='PyramidVisionTransformerV2',
        # depth=50,
        # num_stages=4,
        # out_indices=(0, 1, 2, 3),
        # frozen_stages=1,
        # norm_cfg=dict(type='BN', requires_grad=True),
        # norm_eval=True,
        # style='pytorch',
        num_layers=[2, 2, 2, 2],
        embed_dims=64,
        init_cfg=dict(checkpoint='/disk2t/fby/pvt_v2_b1.pth')),
    neck=dict(
        type='FPNDcnLconv3Dcn',
        out_channels=256,
        start_level=1,
        add_extra_convs='on_input',
        num_outs=5,
        in_channels = [64, 128, 320, 512],
        relu_before_extra_convs = True),
    bbox_head=dict(
        type='FCOSReidHead',
        num_classes=1,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        strides=[8,
                 # 16, 32, 64, 128
                 ],
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))
# training and testing settings
train_cfg = dict(
    assigner=dict(
        type='MaxIoUAssigner',
        pos_iou_thr=0.5,
        neg_iou_thr=0.4,
        min_pos_iou=0,
        ignore_iof_thr=-1),
    allowed_border=-1,
    pos_weight=-1,
    debug=False)
test_cfg = dict(
    nms_pre=1000,
    min_bbox_size=0,
    score_thr=0.05,
    nms=dict(type='nms', iou_threshold=0.5),
    max_per_img=100)
img_norm_cfg = dict(
    mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_ids']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1000, 600),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    samples_per_gpu=4,
    workers_per_gpu=4,
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001)
# optimizer = dict(
#     type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
optimizer_config = dict(
    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
    policy='step',
    warmup='constant',
    warmup_iters=500,
    warmup_ratio=1.0 / 3,
    step=[8, 11])
total_epochs = 12